Heating, ventilation and air conditioning (HVAC) systems can account for a large fraction, e.g. 50 to 70%, of the energy use of a building, and the amount and cost of energy used by HVAC systems scales strongly with air flow (characterized by “air change rate” (ACR) or “air changes per hour” (ACH) for rooms, as well as cubic feet per minute (CFM) for actual air flow rates). The reason for this strong correlation is that in many buildings, room temperature control is achieved by modulating conditioned air flow. In many older buildings, designed when energy was cheap, design engineers found it easy to meet the multiple objectives of desired temperature and humidity levels and healthy fresh air ventilation requirements by using high airflow rates, which entailed high energy use for HVAC. However, from a sustainability viewpoint these high air flows (quantified in terms of the overall ACH of the building) are costly, not only due to the thermal energy used to heat the air and electricity used for cooling, but also the large electricity demand from the supply and return fans used to move the air around the building.
Known models of HVAC systems are used for a variety of purposes. There are two general classes of building HVAC modeling approaches: forward modeling driven by detailed data on building and occupancy characteristics and climate conditions; and inverse modeling based on System Identification (SI) and driven by actual building performance. Building simulations based on forward modeling are most beneficial when used for parametric studies during the design phase, but they require very detailed building and equipment characteristics as inputs. Forward models are difficult to use to determine actual air flow rates since typically they are based on assumed air flow rates. They are often poor predictors of actual building energy use because of changes to the equipment performance, occupancy and usage patterns that are difficult to predict during the design phase. DOE-2 and EQUEST, Trnsys, TRACE, BLAST, and the newer EnergyPlus are examples of commercial software that can be used for building HVAC system analysis. Inverse models are often simpler than detailed building simulations, and are most useful in existing buildings with Energy Management Systems, and can be predictors of future energy in a statistical sense. However, they do not typical use internal physical variables that are important to know in order to improve HVAC system performance.